BiLoRA: A Bi-level Optimization Framework for Overfitting-Resilient Low-Rank Adaptation of Large Pre-trained Models
Khái niệm cốt lõi
BiLoRA introduces a bi-level optimization framework to address overfitting in low-rank adaptation during fine-tuning, enhancing model generalization in natural language tasks.
Tóm tắt
BiLoRA aims to mitigate overfitting in LoRA methods by separating the training of pseudo singular vectors and values on different sub-datasets.
The method significantly outperforms LoRA and AdaLoRA, reducing training time while improving performance across NLU and NLG tasks.
Experiments demonstrate the effectiveness of BiLoRA in enhancing model generalization capabilities.
Impact Statements highlight the potential implications of BiLoRA for advancing Machine Learning applications.